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A Bayesian’s journey to a better research workflow

Konstantinos Vamvourellis
London School of Economics and Political Science

Marianne Corvellec
Institute for Globally Distributed Open Research and Education (IGDORE)



This work began when the two authors met at a software development meeting. Konstantinos was building Bayesian models in his research and wanted to learn how to better manage his research process. Marianne was working on data analysis workflows in industry and wanted to learn more about Bayesian statistics. In this paper, the authors present a Bayesian scientific research workflow for statistical analysis. Drawing on a case study in clinical trials, they demonstrate lessons that other scientists, not necessarily Bayesian, could find useful in their own work. Notably, they can be used to improve productivity and reproducibility in any computational research project.


Bayesian statistics, life sciences, clinical trials, probabilistic programming, Stan, PyStan



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